{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T23:02:31Z","timestamp":1785538951002,"version":"3.56.0"},"reference-count":35,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:00:00Z","timestamp":1710374400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004332","name":"Seoul National University Hospital","doi-asserted-by":"publisher","award":["03-2022-2040"],"award-info":[{"award-number":["03-2022-2040"]}],"id":[{"id":"10.13039\/501100004332","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003696","name":"Electronics and Telecommunications Research Institute","doi-asserted-by":"publisher","award":["23ZR1100"],"award-info":[{"award-number":["23ZR1100"]}],"id":[{"id":"10.13039\/501100003696","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research fund donated by Hyun Hee Kim"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>There is an increasing need for an objective grading system to evaluate the severity of dry eye disease (DED). In this study, a fully automated deep learning-based system for the assessment of DED severity was developed. Corneal fluorescein staining (CFS) images of DED patients from one hospital for system development (n = 1400) and from another hospital for external validation (n = 94) were collected. Three experts graded the CFS images using NEI scale, and the median value was used as ground truth. The system was developed in three steps: (1) corneal segmentation, (2) CFS candidate region classification, and (3) estimation of NEI grades by CFS density map generation. Also, two images taken on different days in 50 eyes (100 images) were compared to evaluate the probability of improvement or deterioration. The Dice coefficient of the segmentation model was 0.962. The correlation between the system and the ground truth data was 0.868 (p&lt;0.001) and 0.863 (p&lt;0.001) for the internal and external validation datasets, respectively. The agreement rate for improvement or deterioration was 88% (44\/50). The fully automated deep learning-based grading system for DED severity can evaluate the CFS score with high accuracy and thus may have potential for clinical application.<\/jats:p>","DOI":"10.1371\/journal.pone.0299776","type":"journal-article","created":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T17:42:12Z","timestamp":1710438132000},"page":"e0299776","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":17,"title":["Deep learning-based fully automated grading system for dry eye disease severity"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5622-2559","authenticated-orcid":true,"given":"Seonghwan","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daseul","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youmin","family":"Shin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mee Kum","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyun Sun","family":"Jeon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young-Gon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2203-5709","authenticated-orcid":true,"given":"Chang Ho","family":"Yoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,3,14]]},"reference":[{"issue":"5","key":"pone.0299776.ref001","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1038\/s41591-021-01343-4","article-title":"Deep learning in histopathology: the path to the clinic","volume":"27","author":"J van der Laak","year":"2021","journal-title":"Nat Med"},{"issue":"1","key":"pone.0299776.ref002","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"EJ Topol","year":"2019","journal-title":"Nat Med"},{"issue":"7","key":"pone.0299776.ref003","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1038\/s41569-020-00503-2","article-title":"Artificial intelligence-enhanced electrocardiography in cardiovascular disease management.","volume":"18","author":"KC Siontis","year":"2021","journal-title":"Nat Rev Cardiol"},{"issue":"9","key":"pone.0299776.ref004","doi-asserted-by":"crossref","first-page":"1410","DOI":"10.1016\/j.ophtha.2018.02.037","article-title":"A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography","volume":"125","author":"F Grassmann","year":"2018","journal-title":"Ophthalmology"},{"issue":"5","key":"pone.0299776.ref005","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/j.ophtha.2020.09.025","article-title":"Quantitative Analysis of OCT for Neovascular Age-Related Macular Degeneration Using Deep Learning","volume":"128","author":"G Moraes","year":"2021","journal-title":"Ophthalmology"},{"issue":"1","key":"pone.0299776.ref006","doi-asserted-by":"crossref","first-page":"14424","DOI":"10.1038\/s41598-020-71425-9","article-title":"A deep learning approach in diagnosing fungal keratitis based on corneal photographs.","volume":"10","author":"MT Kuo","year":"2020","journal-title":"Sci Rep."},{"issue":"3","key":"pone.0299776.ref007","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.jtos.2017.05.011","article-title":"TFOS DEWS II pathophysiology report.","volume":"15","author":"AJ Bron","year":"2017","journal-title":"Ocul Surf."},{"issue":"3","key":"pone.0299776.ref008","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.jtos.2017.05.008","article-title":"TFOS DEWS II Definition and Classification Report.","volume":"15","author":"JP Craig","year":"2017","journal-title":"Ocul Surf."},{"key":"pone.0299776.ref009","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ajo.2019.01.026","article-title":"Estimated Prevalence and Incidence of Dry Eye Disease Based on Coding Analysis of a Large, All-age United States Health Care System","volume":"202","author":"R Dana","year":"2019","journal-title":"Am J Ophthalmol"},{"key":"pone.0299776.ref010","doi-asserted-by":"crossref","first-page":"788545","DOI":"10.3389\/fmed.2021.788545","article-title":"Dry Eye Disease Among Mongolian and Han Older Adults in Grasslands of Northern China: Prevalence, Associated Factors, and Vision-Related Quality of Life.","volume":"8","author":"J Wu","year":"2021","journal-title":"Front Med (Lausanne)."},{"key":"pone.0299776.ref011","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.preteyeres.2014.10.001","article-title":"Clinical staining of the ocular surface: mechanisms and interpretations.","volume":"44","author":"AJ Bron","year":"2015","journal-title":"Prog Retin Eye Res"},{"issue":"5","key":"pone.0299776.ref012","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1080\/02713683.2020.1836226","article-title":"A Comprehensive Review of the Clinical Trials Conducted for Dry Eye Disease and the Impact of the Vehicle Comparators in These Trials","volume":"46","author":"KK Nichols","year":"2021","journal-title":"Curr Eye Res"},{"issue":"4","key":"pone.0299776.ref013","first-page":"221","article-title":"Report of the National Eye Institute\/Industry workshop on Clinical Trials in Dry Eyes","volume":"21","author":"MA Lemp","year":"1995","journal-title":"Clao j."},{"key":"pone.0299776.ref014","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.cct.2018.06.002","article-title":"Dry Eye Assessment and Management (DREAM\u00a9) Study: Study design and baseline characteristics.","volume":"71","author":"PA Asbell","year":"2018","journal-title":"Contemp Clin Trials."},{"issue":"10","key":"pone.0299776.ref015","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1016\/j.ophtha.2021.03.030","article-title":"Systemic Conditions Associated with Severity of Dry Eye Signs and Symptoms in the Dry Eye Assessment and Management Study","volume":"128","author":"K Yu","year":"2021","journal-title":"Ophthalmology"},{"issue":"6","key":"pone.0299776.ref016","doi-asserted-by":"crossref","first-page":"BIO168","DOI":"10.1167\/iovs.17-21831","article-title":"Evaluating Corneal Fluorescein Staining Using a Novel Automated Method","volume":"58","author":"F Amparo","year":"2017","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"11","key":"pone.0299776.ref017","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s10916-018-1086-3","article-title":"An Automated Grading and Diagnosis System for Evaluation of Dry Eye Syndrome.","volume":"42","author":"A Bagbaba","year":"2018","journal-title":"J Med Syst."},{"issue":"12","key":"pone.0299776.ref018","doi-asserted-by":"crossref","first-page":"7896","DOI":"10.1167\/iovs.14-15618","article-title":"Objective assessment of corneal staining using digital image analysis","volume":"55","author":"YS Chun","year":"2014","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"pone.0299776.ref019","article-title":"Fully automated grading system for the evaluation of punctate epithelial erosions using deep neural networks","author":"JH Qu","year":"2021","journal-title":"Br J Ophthalmol"},{"issue":"3","key":"pone.0299776.ref020","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1109\/JSEN.2019.2948576","article-title":"Superficial Punctate Keratitis Grading for Dry Eye Screening Using Deep Convolutional Neural Networks","volume":"20","author":"T-Y Su","year":"2020","journal-title":"IEEE Sensors Journal"},{"issue":"3","key":"pone.0299776.ref021","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/S0734-189X(87)80186-X","article-title":"Adaptive histogram equalization and its variations","volume":"39","author":"SM Pizer","year":"1987","journal-title":"Computer vision, graphics, and image processing"},{"key":"pone.0299776.ref022","volume-title":"International Conference on Medical image computing and computer-assisted intervention;","author":"Springer","year":"2015"},{"issue":"3","key":"pone.0299776.ref023","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the amount of ecologic association between species","volume":"26","author":"LR Dice","year":"1945","journal-title":"Ecology"},{"key":"pone.0299776.ref024","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1214\/aoms\/1177729586","article-title":"A stochastic approximation method","author":"H Robbins","year":"1951","journal-title":"The annals of mathematical statistics"},{"issue":"6088","key":"pone.0299776.ref025","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"DE Rumelhart","year":"1986","journal-title":"nature"},{"issue":"1","key":"pone.0299776.ref026","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1145\/361237.361242","article-title":"Use of the Hough transformation to detect lines and curves in pictures","volume":"15","author":"RO Duda","year":"1972","journal-title":"Communications of the ACM"},{"key":"pone.0299776.ref027","article-title":"Very deep convolutional networks for large-scale image recognition.","author":"K Simonyan","year":"2014","journal-title":"arXiv preprint arXiv:14091556."},{"key":"pone.0299776.ref028","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z, editors. Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition; 2016.","DOI":"10.1109\/CVPR.2016.308"},{"key":"pone.0299776.ref029","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L, editors. Imagenet: A large-scale hierarchical image database. 2009 IEEE conference on computer vision and pattern recognition; 2009: Ieee.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"pone.0299776.ref030","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R,Parikh D, Batra D, editors. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. 2017 IEEE International Conference on Computer Vision; 2017: Ieee.","DOI":"10.1109\/ICCV.2017.74"},{"issue":"3","key":"pone.0299776.ref031","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.ajo.2009.09.013","article-title":"A simplified quantitative method for assessing keratoconjunctivitis sicca from the Sj\u00f6gren\u2019s Syndrome International Registry","volume":"149","author":"JP Whitcher","year":"2010","journal-title":"Am J Ophthalmol"},{"issue":"7","key":"pone.0299776.ref032","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1097\/00003226-200310000-00008","article-title":"Grading of corneal and conjunctival staining in the context of other dry eye tests","volume":"22","author":"AJ Bron","year":"2003","journal-title":"Cornea"},{"issue":"3","key":"pone.0299776.ref033","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1109\/JSEN.2019.2948576","article-title":"Superficial punctate keratitis grading for dry eye screening using deep convolutional neural networks","volume":"20","author":"T-Y Su","year":"2019","journal-title":"IEEE Sensors Journal"},{"issue":"23","key":"pone.0299776.ref034","doi-asserted-by":"crossref","first-page":"3533","DOI":"10.3390\/diagnostics13233533","article-title":"An Automated Grading System Based on Topological Features for the Evaluation of Corneal Fluorescein Staining in Dry Eye Disease.","volume":"13","author":"J Feng","year":"2023","journal-title":"Diagnostics"},{"issue":"4","key":"pone.0299776.ref035","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.ajo.2006.04.062","article-title":"Optimization of anterior eye fluorescein viewing","volume":"142","author":"RC Peterson","year":"2006","journal-title":"American journal of ophthalmology"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0299776","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T17:42:41Z","timestamp":1710438161000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0299776"}},"subtitle":[],"editor":[{"given":"Daniel Duck-Jin","family":"Hwang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,3,14]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3,14]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0299776","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,14]]}}}